Before we Begin:

Recruiting a candidate is a hectic and time consuming process. Recruiters mostly spends lot of time in shortlisting candidates. I created a prototype for visualizing data for easy and quick analysis.

1.Introduction

Campus recruitment is a strategy for sourcing, engaging and hiring young talent for internship and entry-level positions. College recruiting is typically a tactic for medium- to large-sized companies with high-volume recruiting needs, but can range from small efforts (like working with university career centers to source potential candidates) to large-scale operations (like visiting a wide array of colleges and attending recruiting events throughout the spring and fall semester). Campus recruitment often involves working with university career services centers and attending career fairs to meet in-person with college students and recent graduates.

Context of Dataset: Our dataset revolves around the placement season of a Business School in India. Where it has various factors on candidates getting hired such as work experience,exam percentage etc., Finally it contains the status of recruitment and remuneration details.

2.Kernel Goals

There are three primary goals of this kernel.

• Do a exploratory analysis of the Recruitment dataset
• Do an visualization analysis of the Recruitment dataset

3. Importing libraries and exploring Data

3a.Importing Libraries

Python is a fantastic language with a vibrant community that produces many amazing libraries. I am not a big fan of importing everything at once for the newcomers. So, I am going to introduce a few necessary libraries for now, and as we go on, we will keep unboxing new libraries when it seems appropriate

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline

3b.Extracting dataset

#Loading the single csv file to a variable named 'placement'
placement=pd.read_csv("data.csv")

3c. Examining the dataset

placement_copy=placement.copy()
placement_copy.head()

	sl_no	gender	ssc_p	ssc_b	hsc_p	hsc_b	hsc_s	degree_p	degree_t	workex	etest_p	specialisation	mba_p	status	salary
0	1	M	67.00	Others	91.00	Others	Commerce	58.00	Sci&Tech	No	55.0	Mkt&HR	58.80	Placed	270000.0
1	2	M	79.33	Central	78.33	Others	Science	77.48	Sci&Tech	Yes	86.5	Mkt&Fin	66.28	Placed	200000.0
2	3	M	65.00	Central	68.00	Central	Arts	64.00	Comm&Mgmt	No	75.0	Mkt&Fin	57.80	Placed	250000.0
3	4	M	56.00	Central	52.00	Central	Science	52.00	Sci&Tech	No	66.0	Mkt&HR	59.43	Not Placed	NaN
4	5	M	85.80	Central	73.60	Central	Commerce	73.30	Comm&Mgmt	No	96.8	Mkt&Fin	55.50	Placed	425000.0

Inference

• We have Gender and Educational qualification data
• We have all the educational performance(score) data
• We have the status of placement and salary details
• We can expect null values in salary as candidates who weren't placed would have no salary
• Status of placement is our target variable rest of them are independent variable except salary

print ("The shape of the  data is (row, column):"+ str(placement.shape))
print (placement_copy.info())

The shape of the  data is (row, column):(215, 15)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 215 entries, 0 to 214
Data columns (total 15 columns):
 #   Column          Non-Null Count  Dtype  
---  ------          --------------  -----  
 0   sl_no           215 non-null    int64  
 1   gender          215 non-null    object 
 2   ssc_p           215 non-null    float64
 3   ssc_b           215 non-null    object 
 4   hsc_p           215 non-null    float64
 5   hsc_b           215 non-null    object 
 6   hsc_s           215 non-null    object 
 7   degree_p        215 non-null    float64
 8   degree_t        215 non-null    object 
 9   workex          215 non-null    object 
 10  etest_p         215 non-null    float64
 11  specialisation  215 non-null    object 
 12  mba_p           215 non-null    float64
 13  status          215 non-null    object 
 14  salary          148 non-null    float64
dtypes: float64(6), int64(1), object(8)
memory usage: 25.3+ KB
None

We have 215 candidate details and there are mixed datatypes in each column. We have few missing values in the salary column as expected since those are the people who didn't get hired

#Looking at the datatypes of each factor
placement_copy.dtypes

sl_no               int64
gender             object
ssc_p             float64
ssc_b              object
hsc_p             float64
hsc_b              object
hsc_s              object
degree_p          float64
degree_t           object
workex             object
etest_p           float64
specialisation     object
mba_p             float64
status             object
salary            float64
dtype: object

We have 1 integer,5 float and 8 object datatypes in our dataset

3d.Checking for missing data

Datasets in the real world are often messy, However, this dataset is almost clean and simple. Lets analyze and see what we have here.

import missingno as msno 
msno.matrix(placement)

<AxesSubplot:>

As per our inference, we can visualize the null values in salary. Let's see the count

print('Data columns with null values:',placement_copy.isnull().sum(), sep = '\n')

Data columns with null values:
sl_no              0
gender             0
ssc_p              0
ssc_b              0
hsc_p              0
hsc_b              0
hsc_s              0
degree_p           0
degree_t           0
workex             0
etest_p            0
specialisation     0
mba_p              0
status             0
salary            67
dtype: int64

Inference

• There are 67 null values in our data, which means 67 unhired candidates.
• We can't drop these values as this will provide a valuable information on why candidates failed to get hired.
• We can't impute it with mean/median values and it will go against the context of this dataset and it will show unhired candidates got salary.
• Our best way to deal with these null values is to impute it with '0' which shows they don't have any income

4. Data Cleaning

4a. Handling mis_ing values

First lets focus on the missing data in review features,if we drop the rows which has null values we might sabotage some potential information from the dataset. So we have to impute values into the NaN records which leads us to accurate models. Since it is a salary feature,it is best to impute the records with '0' for unhired candidates

placement_copy['salary'].fillna(value=0, inplace=True)
print('Salary column with null values:',placement_copy['salary'].isnull().sum(), sep = '\n')

Salary column with null values:
0

Yayy ! we have cleared that Salary with zero null values. Now it's time to drop unwanted features !

placement_copy.drop(['sl_no','ssc_b','hsc_b'], axis = 1,inplace=True) 
placement_copy.head()

	gender	ssc_p	hsc_p	hsc_s	degree_p	degree_t	workex	etest_p	specialisation	mba_p	status	salary
0	M	67.00	91.00	Commerce	58.00	Sci&Tech	No	55.0	Mkt&HR	58.80	Placed	270000.0
1	M	79.33	78.33	Science	77.48	Sci&Tech	Yes	86.5	Mkt&Fin	66.28	Placed	200000.0
2	M	65.00	68.00	Arts	64.00	Comm&Mgmt	No	75.0	Mkt&Fin	57.80	Placed	250000.0
3	M	56.00	52.00	Science	52.00	Sci&Tech	No	66.0	Mkt&HR	59.43	Not Placed	0.0
4	M	85.80	73.60	Commerce	73.30	Comm&Mgmt	No	96.8	Mkt&Fin	55.50	Placed	425000.0

We have dropped serial number as we have index as default and we have dropped the boards of school education as I believe it doesn't matter for recruitment

4b. ....... Outliers

Outliers are unusual values in your dataset, and they can distort statistical analyses and violate their assumptions. Unfortunately, all analysts will confront outliers and be forced to make decisions about what to do with them. Given the problems they can cause, you might think that it’s best to remove them from your data. But, that’s not always the case. Removing outliers is legitimate only for specific reasons.Outliers can be very informative about the subject-area and data collection process. It’s essential to understand how outliers occur and whether they might happen again as a normal part of the process or study area. Unfortunately, resisting the temptation to remove outliers inappropriately can be difficult. Outliers increase the variability in your data, which decreases statistical power. Consequently, excluding outliers can cause your results to become statistically significant. In our case, let's first visualize our data and decide on what to do with the outliers

plt.figure(figsize = (15, 10))
plt.style.use('seaborn-white')
ax=plt.subplot(221)
plt.boxplot(placement_copy['ssc_p'])
ax.set_title('Secondary school percentage')
ax=plt.subplot(222)
plt.boxplot(placement_copy['hsc_p'])
ax.set_title('Higher Secondary school percentage')
ax=plt.subplot(223)
plt.boxplot(placement_copy['degree_p'])
ax.set_title('UG Degree percentage')
ax=plt.subplot(224)
plt.boxplot(placement_copy['etest_p'])
ax.set_title('Employability percentage')

Text(0.5, 1.0, 'Employability percentage')

As you see, we have very less number of outliers in our features. Especially we have majority of the outliers in hsc percentage Let's clear em up!

Q1 = placement_copy['hsc_p'].quantile(0.25)
Q3 = placement_copy['hsc_p'].quantile(0.75)
IQR = Q3 - Q1    #IQR is interquartile range. 

filter = (placement_copy['hsc_p'] >= Q1 - 1.5 * IQR) & (placement_copy['hsc_p'] <= Q3 + 1.5 *IQR)
placement_filtered=placement_copy.loc[filter]

plt.figure(figsize = (15, 5))
plt.style.use('seaborn-white')
ax=plt.subplot(121)
plt.boxplot(placement_copy['hsc_p'])
ax.set_title('Before removing outliers(hsc_p)')
ax=plt.subplot(122)
plt.boxplot(placement_filtered['hsc_p'])
ax.set_title('After removing outliers(hsc_p)')

Text(0.5, 1.0, 'After removing outliers(hsc_p)')

Voalá! We have removed the outliers

5.Data Visualizations

5a. Count of categorical features- Count plot

plt.figure(figsize = (15, 7))
plt.style.use('seaborn-white')

#Specialisation
plt.subplot(234)
ax=sns.countplot(x="specialisation", data=placement_filtered, facecolor=(0, 0, 0, 0),
                 linewidth=5,edgecolor=sns.color_palette("magma", 3))
fig = plt.gcf()
fig.set_size_inches(10,10)
ax.set_xticklabels(ax.get_xticklabels(),fontsize=12)

#Work experience
plt.subplot(235)
ax=sns.countplot(x="workex", data=placement_filtered, facecolor=(0, 0, 0, 0),
                 linewidth=5,edgecolor=sns.color_palette("cividis", 3))
fig = plt.gcf()
fig.set_size_inches(10,10)
ax.set_xticklabels(ax.get_xticklabels(),fontsize=12)

#Degree type
plt.subplot(233)
ax=sns.countplot(x="degree_t", data=placement_filtered, facecolor=(0, 0, 0, 0),
                 linewidth=5,edgecolor=sns.color_palette("viridis", 3))
fig = plt.gcf()
fig.set_size_inches(10,10)
ax.set_xticklabels(ax.get_xticklabels(),fontsize=12,rotation=20)

#Gender
plt.subplot(231)
ax=sns.countplot(x="gender", data=placement_filtered, facecolor=(0, 0, 0, 0),
                 linewidth=5,edgecolor=sns.color_palette("hot", 3))
fig = plt.gcf()
fig.set_size_inches(10,10)
ax.set_xticklabels(ax.get_xticklabels(),fontsize=12)

#Higher secondary specialisation
plt.subplot(232)
ax=sns.countplot(x="hsc_s", data=placement_filtered, facecolor=(0, 0, 0, 0),
                 linewidth=5,edgecolor=sns.color_palette("rocket", 3))
fig = plt.gcf()
fig.set_size_inches(10,10)
ax.set_xticklabels(ax.get_xticklabels(),fontsize=12)

#Status of recruitment
plt.subplot(236)
ax=sns.countplot(x="status", data=placement_filtered, facecolor=(0, 0, 0, 0),
                 linewidth=5,edgecolor=sns.color_palette("copper", 3))
fig = plt.gcf()
fig.set_size_inches(10,10)
ax.set_xticklabels(ax.get_xticklabels(),fontsize=12)

[Text(0, 0, 'Placed'), Text(1, 0, 'Not Placed')]

Inference

• We have more male candidates than female
• We have candidates who did commerce as their hsc course and as well as undergrad
• Science background candidates are the second highest in both the cases
• Candidates from Marketing and Finance dual specialization are high
• Most of our candidates from our dataset don't have any work experience
• Most of our candidates from our dataset got placed in a company

5b. Distribution Salary- Placed Students

sns.set(rc={'figure.figsize':(12,8)})
f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={"height_ratios": (.15, .85)})

placement_placed = placement_filtered[placement_filtered.salary != 0]
sns.boxplot(placement_placed["salary"], ax=ax_box)
sns.distplot(placement_placed["salary"], ax=ax_hist)
 
# Remove x axis name for the boxplot
ax_box.set(xlabel='')

C:\Users\HOVT\AppData\Local\Programs\Python\Python39\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(
C:\Users\HOVT\AppData\Local\Programs\Python\Python39\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

[Text(0.5, 0, '')]

Inference

• Many candidates who got placed received package between 2L-4L PA
• Only one candidate got around 10L PA
• The average of the salary is a little more than 2LPA

5c. Employability score vs Salary- Joint plot

sns.set(rc={'figure.figsize':(12,8)})
sns.set(style="white", color_codes=True)
sns.jointplot(x=placement_filtered["etest_p"], y=placement_filtered["salary"], kind='kde', color="skyblue")

<seaborn.axisgrid.JointGrid at 0x1e38de21c70>

Inference

• Most of the candidates scored around 60 percentage got a decent package of around 3 lakhs PA
• Not many candidates received salary more than 4 lakhs PA
• The bottom dense part shows the candidates who were not placed

5d.Distribution of all percentages

plt.figure(figsize = (15, 7))
plt.style.use('seaborn-white')
plt.subplot(231)
sns.distplot(placement_filtered['ssc_p'])
fig = plt.gcf()
fig.set_size_inches(10,10)

plt.subplot(232)
sns.distplot(placement_filtered['hsc_p'])
fig = plt.gcf()
fig.set_size_inches(10,10)

plt.subplot(233)
sns.distplot(placement_filtered['degree_p'])
fig = plt.gcf()
fig.set_size_inches(10,10)

plt.subplot(234)
sns.distplot(placement_filtered['etest_p'])
fig = plt.gcf()
fig.set_size_inches(10,10)

plt.subplot(235)
sns.distplot(placement_filtered['mba_p'])
fig = plt.gcf()
fig.set_size_inches(10,10)

plt.subplot(236)
sns.distplot(placement_placed['salary'])
fig = plt.gcf()
fig.set_size_inches(10,10)

C:\Users\HOVT\AppData\Local\Programs\Python\Python39\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
C:\Users\HOVT\AppData\Local\Programs\Python\Python39\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
C:\Users\HOVT\AppData\Local\Programs\Python\Python39\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
C:\Users\HOVT\AppData\Local\Programs\Python\Python39\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
C:\Users\HOVT\AppData\Local\Programs\Python\Python39\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
C:\Users\HOVT\AppData\Local\Programs\Python\Python39\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

Inference

• All the distributions follow normal distribution except salary feature
• Most of the candidates educational performances are between 60-80%
• Salary distribution got outliers where few have got salary of 7.5L and 10L PA

5e.Work experience Vs Placement Status

#Code forked from-https://www.kaggle.com/biphili/hospitality-in-era-of-airbnb
plt.style.use('seaborn-white')
f,ax=plt.subplots(1,2,figsize=(18,8))
placement_filtered['workex'].value_counts().plot.pie(explode=[0,0.05],autopct='%1.1f%%',ax=ax[0],shadow=True)
ax[0].set_title('Work experience')
sns.countplot(x = 'workex',hue = "status",data = placement_filtered)
ax[1].set_title('Influence of experience on placement')
plt.show()

Inference

• We have nearly 66.2% of candidates who never had any work experience
• Candidates who never had work experience have got hired more than the ones who had experience
• We can conclude that work experience doesn't influence a candidate in the recruitment process

5f. MBA marks vs Placement Status- Does your academic score influence?

g = sns.boxplot(y = "status",x = 'mba_p',data = placement_filtered, whis=np.inf)
g = sns.swarmplot(y = "status",x = 'mba_p',data = placement_filtered, size = 7,color = 'black')
sns.despine()
g.figure.set_size_inches(12,8)
plt.show()

Inference
Comparitively there's a slight difference between the percentage scores between both the groups, But still placed candidates still has an upper hand when it comes to numbers as you can see in the swarm. So as per the plot,percentage do influence the placement status

5g.Does MBA percentage and Employability score correlate?

import plotly_express as px
gapminder=px.data.gapminder()
px.scatter(placement_filtered,x="mba_p",y="etest_p",color="status",facet_col="workex")

Inference

• There is no relation between mba percentage and employability test
• There are many candidates who haven't got placed when they don't have work experience
• Most of the candidates who performed better in both tests have got placed

5h. Is there any gender bias while offering remuneration?

px.violin(placement_placed,y="salary",x="specialisation",color="gender",box=True,points="all")

Inference

• The top salaries were given to male
• The average salary offered were also higher for male
• More male candidates were placed compared to female candidates

5i. Coorelation between academic percentages

sns.heatmap(placement_placed.corr(),annot=True,fmt='.1g',cmap='Greys')

<AxesSubplot:>

Inference

• Candidates who were good in their academics performed well throughout school,undergrad,mba and even employability test
• These percentages don't have any influence over their salary

5j.Distribution of our data

sns.pairplot(placement_filtered,vars=['ssc_p','hsc_p','degree_p','mba_p','etest_p'],hue="status")

<seaborn.axisgrid.PairGrid at 0x1e38ba9f880>

Inference

• Candidates who has high score in higher secondary and undergrad got placed
• Whomever got high scores in their schools got placed
• Comparing the number of students who got placed candidates who got good mba percentage and employability percentage